Fault detection in flotation processes based on deep learning and support vector machine

Abstract

Effective fault detection techniques can help flotation plant reduce reagents consumption, increase mineral recovery, and reduce labor intensity. Traditional, online fault detection methods during flotation processes have concentrated on extracting a specific froth feature for segmentation, like color, shape, size and texture, always leading to undesirable accuracy and efficiency since the same segmentation algorithm could not be applied to every case. In this work, a new integrated method based on convolution neural network (CNN) combined with transfer learning approach and support vector machine (SVM) is proposed to automatically recognize the flotation condition. To be more specific, CNN function as a trainable feature extractor to process the froth images and SVM is used as a recognizer to implement fault detection. As compared with the existed recognition methods, it turns out that the CNN-SVM model can automatically retrieve features from the raw froth images and perform fault detection with high accuracy. Hence, a CNN-SVM based, real-time flotation monitoring system is proposed for application in an antimony flotation plant in China.

摘要

对浮选过程进行故障诊断有助于选矿厂减少药剂消耗, 增加有效矿物的回收以及降低现场操作 工人的劳动强度等。针对传统的浮选过程故障诊断方法大都是对单一的泡沫特征(如泡沫颜色, 形状, 大小, 纹理等)进行人工提取, 存在精度低, 效率低等缺陷。本文提出一种基于深度学习和支持向量 机的浮选过程故障诊断方法。该模型利用卷积神经网络(CNN) 自动提取泡沫图像特征, 利用支持向量 机(SVM) 根据提取的图像特征给出诊断结果。通过与现存的浮选过程诊断方法相比较, 本文提出的 CNN-SVM 相结合的方法, 测试性能优于其他识别模型。